| title | Evaluating Cognitive Age Alignment in Interactive AI Agents |
|---|---|
| authors | Yifan Shen, Jiawen Zhang, Jian Xu, Junho Kim, Ismini Lourentzou, Xu Cao, Meihuan Huang |
| date | 2026-05-18 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.17894 |
| affiliation | PediaMed AI |
| upvotes | 2 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | While agentic AI and its core multimodal large language models (MLLMs) have demonstrated remarkable promise in language and visual reasoning across domains ranging from daily life to advanced scientific research, a profound gap remains between artificial and human intelligence. Despite the integration of powerful tools and advanced MLLMs, state-of-the-art AI agents frequently fail at foundational, seemingly simple tasks that a child can resolve with ease. Inspired by the Wechsler Intelligence Scale for Children (WISC), we introduce ChildAgentEval, the first psychometrically grounded interactive benchmark for evaluating cognitive age alignment in MLLM-based agents. ChildAgentEval systematically compares the reasoning performance of various MLLM-based interactive agents against age-specific human developmental stages, exposing where current agentic AI systems can and cannot simulate age-specific cognitive behavior. |
cite
@misc{shen2026evaluating,
title={Evaluating Cognitive Age Alignment in Interactive AI Agents},
author={Yifan Shen and Jiawen Zhang and Jian Xu and Junho Kim and Ismini Lourentzou and Xu Cao and Meihuan Huang},
year={2026},
eprint={2605.17894},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.17894}
}